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Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches.
“Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation”
Clive Rodgers · 1976
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“The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics”
F. Rabier et al · 2000
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“STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP”
Neill Bowler, Clive Pierce and Alan Seed · 2006
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“The quiet revolution of numerical weather prediction”
Peter Bauer, Alan Thorpe and Gilbert Brunet · 2015
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“Attention is all you need”
Ashish Vaswani et al · 2017
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“Skilful precipitation nowcasting using deep generative models of radar”
Suman Ravuri et al · 2021
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“Deep learning for twelve hour precipitation forecasts”
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“GenCast: Diffusion-based ensemble forecasting for medium-range weather”
Ilan Price et al · 2023
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“Skilful nowcasting of extreme precipitation with NowcastNet”
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“Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation”, 2024
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“The ERA5 global reanalysis”
Hans Hersbach et al · 2049
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Yuchen Zhang et al · 2023
Cited alongside, same era.